Synthesizing the Unseen for Zero-shot Object Detection

TL;DR

This paper introduces a generative approach to synthesize visual features for unseen classes in zero-shot object detection, enabling the model to recognize both seen and unseen objects effectively.

Contribution

It proposes a novel generative model that synthesizes discriminative and diverse visual features for unseen classes using class semantics, improving zero-shot detection performance.

Findings

Significant improvements on PASCAL VOC, MSCOCO, and ILSVRC detection benchmarks.

Effective handling of unseen classes in both conventional and generalized settings.

Outperforms existing state-of-the-art zero-shot detection methods.

Abstract

The existing zero-shot detection approaches project visual features to the semantic domain for seen objects, hoping to map unseen objects to their corresponding semantics during inference. However, since the unseen objects are never visualized during training, the detection model is skewed towards seen content, thereby labeling unseen as background or a seen class. In this work, we propose to synthesize visual features for unseen classes, so that the model learns both seen and unseen objects in the visual domain. Consequently, the major challenge becomes, how to accurately synthesize unseen objects merely using their class semantics? Towards this ambitious goal, we propose a novel generative model that uses class-semantics to not only generate the features but also to discriminatively separate them. Further, using a unified model, we ensure the synthesized features have high diversity that represents the intra-class differences and variable localization precision in the detected bounding boxes. We test our approach on three object detection benchmarks, PASCAL VOC, MSCOCO, and ILSVRC detection, under both conventional and generalized settings, showing impressive gains over the state-of-the-art methods. Our codes are available at https://github.com/nasir6/zero_shot_detection.